Recently, NB models have been used in systems neuroscience due to their suitability for over-dispersed data [1]}, [2]}.
However, difficulties arise when using Bayesian methods in conjunction with hierarchical NB modeling, therefore, our work addresses three associated contributions. First, we have proposed a shrinkage SODS estimator with a lower MSE than alternative GLM methods, and have validated it using both simulated and experimental data. Second, our proposed method is ideally suited to short over-dispersed spike count data. While, at the same time, our approach avoids the high computational costs often associated with using fully Bayes methods by employing empirical Bayes inference. Third, the SODS method has better performance, when compared with both Poisson regression and NB regression methods, in terms of: (1) identifying interactions among neural populations; and (2) estimating mean spike counts. Our approach can also easily be extended to take more covariates into consideration.
